Bayesian Optimization for Enhancing Spherical Crystallization Derived from Emulsions: A Case Study on Ibuprofen

The pharmaceutical industry is a highly specialized field where strict quality control and accelerated time-to-market are essential for maintaining competitive advantage. Spherical crystallization has emerged as a promising approach in pharmaceutical manufacturing, offering significant potential to reduce equipment and operating costs, enhancing drug bioavailability, and facilitating compliance with product quality regulations. Emulsions, as an enabling technology for spherical crystallization, present unique advantages. However, the quality of spherical crystallization products derived from emulsions is significantly influenced by the intricate interactions between crystallization phenomena, formulation variables, and solution hydrodynamics. These complexities pose substantial challenges in determining optimal operational conditions to achieve the desired product characteristics. In this study, Bayesian optimization (BO) is employed to refine and optimize the operational conditions for the spherical crystallization of a representative drug, ibuprofen. The primary goal is to improve product flowability, measured by angle of repose, while maintaining the median particle size within a specified range. The optimization process focuses on key variables such as temperature, stirring speed, duration, and BSA concentration. With the help of acquisition functions, BO enables the identification of a high-quality product with fewer experimental trials compared with traditional design of experiments (DoE) methods.